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首页主动降噪技术详解:信号处理与应用实战
"Active Noise Control (ANC): A Tutorial Review" 是一篇深入探讨有源噪声控制技术的论文。作者沈国(SENM.KUO)和邓恩·M·摩根(Dennis N. Morgan, IEEE Senior Member)在文中聚焦于实际应用中的主动降噪系统,尤其是针对信号处理算法的实践性和数字信号处理(DSP)在真实世界场景中的实现。 该论文首先介绍了有源噪声控制的基本原理,即通过一系列辅助声源引入相位相反的“反噪声”波,来抵消环境噪声。这些辅助声源通过电子系统连接,并采用特定的信号处理算法来定制噪音消除策略。有源噪声控制的应用范围广泛,包括制造业、工业操作以及消费电子产品等领域。 论文的核心内容是开发并分析单通道宽带前馈控制的基础自适应算法。这个算法随后被扩展到窄带前馈和自适应反馈控制,以应对不同频率范围的噪声。对于多通道情况,作者详细探讨了在线次级路径建模技术,如格子方法、频域方法、子带方法以及递归算法等,这些技术有助于提高系统的性能和鲁棒性。 作者特别强调了实际应用中的挑战,如实时性、准确性以及抗干扰能力,这些都是设计有效ANC系统时必须考虑的关键因素。此外,论文还可能涉及误差分析、系统优化、算法稳定性和计算效率等问题,以及如何在有限的硬件资源下实现高效的主动降噪。 “Active Noise Control: A Tutorial Review”是一篇实用教程,旨在为读者提供从理论到实践的全面指导,帮助理解和掌握有源噪声控制技术在现代生活中的实际应用。通过阅读这篇论文,读者能够深入了解主动降噪系统的各个方面,包括信号处理策略、技术选择以及解决实际问题的策略。
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![](https://csdnimg.cn/release/download_crawler_static/69664106/bg6.jpg)
Fig. 6. Block diagram of ANC system with feedback.
Fig. 7. ANC with acoustic feedback neutralization.
1) Feedback Neutralization: The simplest approach to
solving the feedback problem is to use a separate feedback
cancellation, or “neutralization,” filter within the controller,
which is exactly the same technique as used in acoustic echo
cancellation [43]. This electrical model of the feedback
path is driven by the secondary signal, and its output
is subtracted from the reference sensor signal [44]. A
duct-acoustic ANC system using the FXLMS algorithm
with feedback neutralization is illustrated in Fig. 7. The
feedback component of the reference microphone signal
is canceled electronically using a feedback neutralization
filter
which models the feedback path
Since the primary noise is highly correlated with the
antinoise, the adaptation of the feedback neutralization filter
must be inhibited when the ANC system is in operation,
similar to adaptive echo cancellation during periods of
double talk. Thus, feedback neutralization is achieved, in
effect, by using an offline adaptive method for determining
the transfer function of the feedback path. The models
and can be estimated simultaneously by using the
offline modeling technique [4].
2) Adaptive IIR Filter: Equation (14) shows that when
feedback is present, the optimal solution of the adaptive
filter is generally an IIR function with poles and zeros. This
rational function can be approximated by an FIR function
of sufficient order, but a smaller step size
then has to
be used for stability reasons. The poles of an IIR filter
make it possible to obtain well-matched characteristics with
a lower order structure, thus requiring fewer arithmetic
Fig. 8. ANC system using the filtered-U recursive LMS algo-
rithm.
operations [41]. However, the disadvantages of adaptive
IIR filters are: 1) IIR filters are not unconditionally stable
because of the possibility that some pole(s) of the filter
will move outside of the unit circle during the adaptive
process, causing instability; 2) the adaptation may converge
to a local minimum because the MSE function of adaptive
IIR filters is generally nonquadratic; and 3) IIR adaptive
algorithms can have a relatively slow convergence rate
in comparison with that of FIR filters. A comprehensive
discussion of adaptive IIR filters can be found in the
literature [45], [46].
A block diagram of an adaptive IIR ANC system [47]
is illustrated in Fig. 8. The output signal of the IIR filter
is computed as
(16)
where
is the weight
vector of
is the reference signal vector,
is the weight vector of and
is the output signal vector delayed by one sample.
Many algorithms can be employed to find the optimal set
of coefficients
and to minimize the error signal
In 1976, Feintuch [48] suggested that the recursion based
on the old output gradients is negligible. Based on this
suggestion, the “filtered-U recursive LMS algorithm” [49]
for ANC is derived as [4]
(17)
(18)
where
is the filtered version
of the canceling signal vector at time
In practice, it
is reasonable to use a higher order for
than for
[50]. Real-time experiments have been conducted to test
the system performance for various reference microphone
locations, error microphone locations, and different time-
varying sources, such as a centrifugal fan and diesel engine
[51].
Given the complexities associated with the pole-zero
structure of
and one cannot predict the
values to which
and will converge. Also, global
convergence and stability of the filtered-U recursive LMS
948 PROCEEDINGS OF THE IEEE, VOL. 87, NO. 6, JUNE 1999
Authorized licensed use limited to: UNIVERSITY OF SOUTHAMPTON. Downloaded on August 6, 2009 at 09:24 from IEEE Xplore. Restrictions apply.
![](https://csdnimg.cn/release/download_crawler_static/69664106/bg7.jpg)
Fig. 9. Basic configuration of narrow-band ANC system.
algorithm have never been proven formally. A modified
leaky version of the simplified hyperstable adaptive re-
cursive filter (SHARF) algorithm [52] has been developed
for ANC applications to improve the stability of the IIR
adaptive filter [53]. In that algorithm, a lowpass filter is
used to smooth the error signal for the filtered-U recursive
LMS algorithm, thereby providing a higher stability margin.
III. N
ARROWBAND FEEDFORWARD ANC
Many noises are periodic, such as those generated by
engines, compressors, motors, fans, and propellers. Direct
observation of the mechanical motion of such sources
generally is possible by using an appropriate sensor, which
provides an electrical reference signal that contains the
fundamental frequency and all the harmonics of the primary
noise. However, this technique is only effective for periodic
noise because the fundamental driving frequency is the only
reference information available.
A. Introduction
A basic block diagram of narrow-band ANC for reducing
periodic acoustic noise in a duct is illustrated in Fig. 9. This
system controls harmonic sources by adaptively filtering a
synthesized reference signal
internally generated by
the ANC system. This technique has the following advan-
tages: 1) undesired acoustic feedback from the canceling
loudspeaker back to the reference microphone is avoided;
2) nonlinearities and aging problems associated with the
reference microphone are avoided; 3) the periodicity of the
noise removes the causality constraint; 4) the use of an
internally generated reference signal results in the ability
to control each harmonic independently; and 5) it is only
necessary to model the acoustic plant transfer function over
frequencies in the vicinity of the harmonic tones; thus, an
FIR filter with substantially lower order may be used.
The reference signal generator is triggered by a syn-
chronization pulse from a nonacoustic sensor, such as a
tachometer signal from an automotive engine. In general,
two types of reference signals are commonly used in
narrow-band ANC systems: 1) an impulse train with a
period equal to the inverse of the fundamental frequency
of the periodic noise [54] and 2) sinewaves that have the
same frequencies as the corresponding harmonic tones to
be canceled. The first technique is called the waveform
synthesis method, which was proposed by Chaplin [55].
The second technique embodies the adaptive notch filter,
which was originally developed for the cancellation of tonal
interference [56] and applied to periodic ANC [57].
The waveform synthesis method discussed next in
Section III-B employs synchronous sampling. However,
for some applications, the actual period will vary from
the nominal value as a function of loading conditions.
Therefore, it is sometimes desirable to operate asynchro-
nously with a fixed sampling rate so that the secondary-path
estimate filter coefficients do not have to be changed as
a function of actual machine rotation rate. Also, some
digital signal processors cannot be efficiently utilized on
a synchronous signal-driven basis. Asynchronous ANC
systems using the FXLMS algorithm eliminate the problem
of having to change
as the sampling rate varies and
are implicit in the later formulations of Sections III-C and
III-D.
B. Waveform Synthesis Method
1) Structures and Algorithms: The waveform synthesizer
[55] stores canceling noise waveform samples
in unique contiguous memory addresses,
where
is the number of samples over one cycle of the
waveform and
is the current time index. These samples
represent the required waveform to be generated and are
sequentially sent to a D/A converter to produce the actual
canceling noise waveform for the secondary loudspeaker.
Thus
(19)
represents the
th element of waveform samples, where
mod and can be implemented as a pointer
incremented in a circular fashion between zero and
for each sampling period, controlled by interrupts generated
from the synchronization signal.
The residual noise picked up by the error microphone
is synchronously sampled with the reference signal timing
pulses. In a practical system, there is a delay between the
time the signal
is fed to the loudspeaker
and the time it is received at the error microphone. This
delay can be accommodated by subtracting a time offset
from the circular pointer
Thus, the adaptation unit
adjusts the values of the waveform samples using a variant
of the LMS algorithm
otherwise
(20)
where
and is the time delay, which is
constant for a given loudspeaker-microphone arrangement,
is the sampling period, and greatest integer less
than or equal to
This offset number must be updated
as the sampling rate varies, since it is synchronized with
the noise source.
KUO AND MORGAN: ACTIVE NOISE CONTROL 949
Authorized licensed use limited to: UNIVERSITY OF SOUTHAMPTON. Downloaded on August 6, 2009 at 09:24 from IEEE Xplore. Restrictions apply.
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